Edge Extraction Method of Remote Sensing UAV Terrace Image Based on Topographic Feature

智慧农业

KANG Yang¹  FAN Xiao¹  CHANG Yadong¹  ZHANG Hongming¹  YANG Yanan¹  ZHANG Hanwen² 

[1] College of Information Engineering, Northwest A & F University, Yangling 712100, China;College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, China;

关键词: uav image;    terrace;    edge extraction;    slope;    region segmentation;   

DOI  :  10.12133/j.smartag.2019.1.4.201908-SA005

来源: DOAJ

【 摘 要 】

Terraces achieve water storage and sediment function by slowing down the slope and soil erosion. This kind of terraced or wave-section farmland built along the contour line is a high-yield and stable farmland facility with key construction in the dry farming area. It provides a strong guarantee for increasing grain production and farmers' income. In recent years, Gansu province has carried out a large amount of construction on terraces, however, due to the poor quality of the previous construction and management, the terraced facilities were in danger of being destroyed. In order to prevent and repair the terraces, it is necessary to timely and accurately extract the terrace information. The segmentation of terraces can be obtained by edge extraction, but the effect of satellite data is not ideal. With the continuous development of remote sensing technology of drones, the acquisition of high-precision terrace topographic information has become possible. In this research, the slope was extracted from the digital elevation model data in the data preprocessing stage, and the orthophoto data of the three experimental areas were merged with the corresponding slope data, respectively. Then the rough edge extraction method based on Canny operator and the fine edge extraction method based on multi-scale segmentation were used to perform edge detection on two data sources. Finally, the influence of slope on the extraction of terraced edges of remote sensing images of UAVs was analyzed based on the overall accuracy of edge detection and user accuracy. The experimental results showed that, in the rough edge extraction method, the data source accuracy of the fusion slope and image was improved by 23.97% in the OA precision evaluation, and the average improvement in the user's accuracy was 20.68%. In the fine edge extraction method, the accuracy based on the data source 2 was also increased by 17.84% on average in the overall accuracy evaluation of the data source 1, and by an average of 19.0% in the UV accuracy evaluation. The research shows that in the extraction of terraced edges of UAV remote sensing images, adding certain terrain features can achieve better edge extraction results.

【 授权许可】

Unknown